Traffic cameras are frequently used to assist law enforcement personnel in enforcing traffic laws and regulations. For example, traffic cameras may be positioned to record passing traffic, and the recordings may be analyzed to determine various vehicle characteristics, including vehicle speed, passenger configuration, and other characteristics relevant to traffic rules. Typically, in addition to detecting characteristics related to compliance with traffic rules, traffic cameras are also tasked with recording and analyzing license plates in order to associate detected characteristics with specific vehicles or drivers.
However, law enforcement transportation cameras are often positioned with a view that is suboptimal for multiple applications. As an example, law enforcement transportation cameras may be tasked with both determining the speed of a passing vehicle and capturing the license plate information of the same vehicle for identification purposes. Regulations typically require that license plates be located on the front and/or rear portion of vehicles. As a result, an optimum position for capturing vehicle license plates may be to place the camera such that it has a substantially direct view of either the front portion of an approaching vehicle or the rear portion of a passing vehicle. However, as described below, a direct view of the front or rear portion of a vehicle may not be an optimal view for determining other vehicle characteristics, such as vehicle speed.
For example, as depicted in FIG. 1A, multiple images 110-113 of a vehicle 130 may be captured over a period of time. The speed of vehicle 130 may be determined by analyzing changes 120 in the position of a fixed feature of the vehicle (e.g., its roofline), or by analyzing changes in the size of the vehicle, over time.
However, even if vehicle 130 approaches the camera at a constant speed, such changes in position or size may not occur in a linear manner. Rather, changes in vehicle size or feature position may occur at slower rates when vehicle 130 is far from the camera but at faster rates when vehicle 130 is near to the camera. Similarly, the rate of change may depend on the size of the vehicle. As a result, speed calculations based on images of the front or rear portion of a vehicle, as depicted in FIG. 1A, may need to make certain geometric assumptions, such as vehicle distance or size, in order to control for geometric distortion. And the accuracy of speed calculations will depend on the accuracy of those geometric assumptions.
Similarly, the accuracy of speed determinations may also depend on the accuracy with which a vehicle a particular feature of vehicle 130 is tracked across images. For example, as depicted in FIG. 1A, the change in the size of vehicle 130 as it approaches the camera may be measured by referencing the change in position of a particular feature, such as its roofline or license plate 131. Thus, errors in identifying the same feature across multiple images may also affect the accuracy of speed determinations based thereon.
As a general matter, speed calculations based on rear or frontal views of a vehicle tend to be more susceptible to inaccuracy due to the limitations imposed by the geometric configuration than to errors in tracking vehicle features across images. By contrast, speed calculations based on top-down views of a vehicle tend to be less susceptible to inaccuracy due to the particular geometric configuration being used but more susceptible to errors in tracking vehicle features due to height variations between different vehicles.
For example, as depicted in FIG. 1B, the speed of a vehicle 160 may be determined by measuring the change in lateral position of a fixed feature of the vehicle (e.g., its front bumper) over time, as viewed from a top-down perspective. In some cases, provided that the camera is positioned at an adequate distance from the road, the size of vehicle 160 in each sequential image will change only slightly as it passes through the camera's field of view. As a result, the effect of the geometric configuration on speed calculations from a top-down perspective may be smaller than that of the perspective depicted in FIG. 1A. By contrast, the accuracy of speed calculations may be more susceptible to errors in tracking the same feature of vehicle 160 across different images due to height variations between different vehicles. Moreover, as can be seen, the license plate 161 of vehicle 160 may not be viewable from a top-down view. Similar issues may arise when analyzing sequential images taken of the side portion of a vehicle, which may further be complicated by potential occlusion by the presence of other vehicles.
Given the different challenges of capturing and analyzing vehicle images from a frontal or rear perspective versus a top-down (or side) perspective, one possible enhancement may be to use multiple cameras positioned at different locations such that images of a single vehicle may be captured from multiple, different perspectives. However, such multi-camera systems may impose higher overhead costs due to increased power consumption, increased complexity due to a potential need for temporal and spatial alignment of the imagery, increased communication infrastructure, the need for additional installation and operation permits, and maintenance, among other costs.
Consequently, transportation imaging systems may be improved by techniques for using a single camera to record traffic information from multiple, different optical perspectives simultaneously.